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Related Concept Videos

Life Tables01:22

Life Tables

594
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
594
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

679
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
679
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

472
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
472
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

682
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
682
Actuarial Approach01:20

Actuarial Approach

345
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
345
Hazard Rate01:11

Hazard Rate

467
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
467

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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

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Mortality Patterns among Hospital Deaths.

R K Karki1

  • 1Department of Forensic Medicine and Toxicology, Kathmandu University School of Medical Sciences, Dhulikhel, Kavre, Nepal.

Kathmandu University Medical Journal (KUMJ)
|November 29, 2016
PubMed
Summary
This summary is machine-generated.

Inpatient mortality in Nepal is low, with respiratory diseases being the leading cause of death. Improving death certification accuracy is crucial for public health policy and epidemiological research.

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Area of Science:

  • Medical Research
  • Public Health
  • Epidemiology

Background:

  • Hospital death data is medically certified but often limited in developing countries like Nepal.
  • Understanding inpatient mortality patterns is essential for effective health priority setting.

Purpose of the Study:

  • To determine the pattern, frequency, and causes of inpatient mortality at Dhulikhel Hospital, Nepal.
  • To provide data for improving health strategies and reducing avoidable deaths.

Main Methods:

  • Retrospective study of all deaths (n=247) at Dhulikhel Hospital from January 2012 to December 2013.
  • Review of medical records for all deceased patients.

Main Results:

  • Overall hospital mortality rate was 0.90% (247 deaths out of 26,836 admissions).
  • Deaths were most frequent in the Medicine (52.64%) and Pediatrics (32.38%) departments.
  • Leading causes of death were respiratory diseases (38.87%), infectious diseases (20.64%), and hepatobiliary diseases (16.19%).
  • Mortality was highest at the extremes of life (infants <1 month and adults >60 years).

Conclusions:

  • Hospital death registration data requires validation for epidemiological research and policy.
  • Streamlining death certification and cause-of-death coding is necessary to improve data reliability.
  • Cause-specific mortality data can inform interventions to reduce avoidable hospital deaths.